Financial analytics for interlinking stock market and macroeconomic performance- post financial crisis 2008

Q4 Mathematics
Anjali Bhute
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引用次数: 1

Abstract

Financial analytics has been highly crucial in forecasting possible future economic scenarios. The relationship between a country’s macroeconomic indicators and its stock market has been extensively studied in the literature. Stock prices should be used as leading indications of future economic activity if they accurately reflect the underlying fundamentals. On the contrary, if economic activity follows stock price movement, the outcomes should be the opposite, i.e., economic activity should lead stock price movement. The paper attempts to make use of financial descriptive analytics to explore the interconnection between prominent macroeconomic indicators and stock market activity post ten years of financial crisis 2008. The study’s range is constrained to explore the aforementioned interconnection for the period from September’ 2008 to August’ 2018. The following factors have been found to be related over the long term: GDP, Production Index, Inflation, Exchange Rate, Money Supply, Imports, Exports, FDI, and Stock Market Returns. Shockingly FII has not shown any cointegrating equation. Also causality was observed between stock market and economic indicators. Impulse Response Function (IRF) and Variance Decomposition (VDC) techniques of VAR model are applied to decompose or fractionalize the variability caused by macroeconomic indicators on the BSE Sensex returns which has given some interesting results.
金融分析连结股票市场和宏观经济表现- 2008年金融危机后
金融分析在预测未来可能出现的经济情景方面非常重要。一个国家的宏观经济指标与其股票市场之间的关系在文献中得到了广泛的研究。如果股票价格准确地反映了潜在的基本面,那么它们就应该被用作未来经济活动的领先指标。相反,如果经济活动跟随股票价格变动,那么结果应该是相反的,即经济活动应该引导股票价格变动。本文试图利用金融描述性分析来探讨2008年金融危机十年后重要宏观经济指标与股市活动之间的相互关系。该研究的范围仅限于探讨上述互连的时间为2008年9月至2018年8月。以下因素被发现是长期相关的:GDP,生产指数,通货膨胀,汇率,货币供应量,进口,出口,外国直接投资和股票市场回报。令人震惊的是,FII没有显示任何协整方程。股市与经济指标之间也存在因果关系。利用VAR模型的脉冲响应函数(IRF)和方差分解(VDC)技术对宏观经济指标对BSE Sensex收益的影响进行分解或分项化,得到了一些有趣的结果。
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来源期刊
Model Assisted Statistics and Applications
Model Assisted Statistics and Applications Mathematics-Applied Mathematics
CiteScore
1.00
自引率
0.00%
发文量
26
期刊介绍: Model Assisted Statistics and Applications is a peer reviewed international journal. Model Assisted Statistics means an improvement of inference and analysis by use of correlated information, or an underlying theoretical or design model. This might be the design, adjustment, estimation, or analytical phase of statistical project. This information may be survey generated or coming from an independent source. Original papers in the field of sampling theory, econometrics, time-series, design of experiments, and multivariate analysis will be preferred. Papers of both applied and theoretical topics are acceptable.
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